Piezo1: Proteins for mechanotransduction and integration of endothelial shear stress & intravascular pressure

Piezo proteins are transmembrane ion channels, specialized in detecting mechanosensitive stimuli and transduce mechanical forces into biochemical signals. Piezo proteins research has helped understand physiological mechanisms, but the integrative role that Piezo1 plays in the regulation of the microvasculature has remained unstudied. Our main objective was to characterize ex vivo microvascular responses to the blockade of Piezo1 mechanotransduction in male (n=29) and female (n=24) Sprague-Dawley (SD) rats. Gracilis arterioles (GA) and middle cerebral arterioles (MCA) were harvested for ex-vivo vessel preparations. After vessel viability confirmation, every vessel was submitted to myogenic and flow challenges under control conditions and after Grammostola Mechanotoxin 4 (GsMTx4) incubation to blocking Piezo1 channels, to quantify the homeostatic response of arterioles before and after Piezo1 antagonism. We are able to report Piezo1 as indispensable component in vascular smooth muscle cells (VSMC) and Endothelial cells (EC) to sense and change vessel diameter based on intravascular pressure and shear stress, correspondingly. Also, we report for the first time a heterogeneous response in males and females after Piezo1 antagonism in representative resistance arterioles from the skeletal muscle and cerebral circulation

INCARCERATION EFFECTS ON ATTAINING HIGHER EDUCATION FOR FORMERLY INCARCERATED YOUTH

Incarceration disrupts areas of a juvenile’s life on multiple levels, including personal, social, and educational. Incarceration can present many obstacles for youth who are in pursuit of furthering their education. This research project sought to assess if the five identified factors, including quality of precollege education, mentoring, reentry services, family supports, and socioeconomic status, played a role in adults, who were formerly incarcerated youth, pursuing higher levels of education. The study utilized an online survey to gather numerical data on the participant’s perception of how they believe these factors influenced them. A bivariate analysis was used to analyze if the identified factors had an influence on the pursuit of higher education for adults who were formerly incarcerated youth. A frequency analysis was completed to determine which of the five factors were perceived to be influential to participants. A bivariate analysis was completed to see if there were any relationships to key demographic variables and level of education. The factors deemed most influential were mentoring programs and family supports. The factors that were deemed least influential were reentry services and precollege education. The research findings have the potential to inform social work professionals of what specific programs and services formerly incarcerated populations can be referred to in order to support them on their educational journey

Dash Sylvereye:A Python Library for Dashboard-Driven Visualization of Large Street Networks

State-of-the-art open graph visualization tools like Gephi, KeyLines, and Cytoscape are not suitable for studying street networks with thousands of roads since they do not support simultaneously polylines for edges, navigable maps, GPU-accelerated rendering, interactivity, and the means for visualizing multivariate data. To fill this gap, we present Dash Sylvereye: a new Python library to produce interactive visualizations of primal street networks on top of tiled web maps. Thanks to its integration with the Dash framework, Dash Sylvereye can be used to develop web dashboards around temporal and multivariate street data. This is achieved by coordinating the various elements of a Dash Sylvereye visualization with other plotting and UI components provided by Dash. Additionally, Dash Sylvereye provides convenient functions to easily import OpenStreetMap street topologies obtained with the OSMnx library. Moreover, Dash Sylvereye uses WebGL for GPU-accelerated rendering when redrawing the road network. We conduct experiments to assess the performance of Dash Sylvereye on a commodity computer when exploiting software acceleration in terms of frames per second, CPU time, and frame duration. We show that Dash Sylvereye can offer fast panning speeds, close to 60 FPS, and CPU times below 20 ms, for street networks with thousands of edges, and above 24 FPS, and CPU times below 40 ms, for networks with dozens of thousands of edges. Additionally, we conduct a performance comparison against two state-of-the-art street visualization tools. We found Dash Sylvereye to be competitive when compared to the state-of-the-art visualization libraries Kepler.gl and city-roads. Finally, we describe a web dashboard application that exploits Dash Sylvereye for the analysis of a SUMO vehicle traffic simulation

Shape evolution and shape coexistence in Pt isotopes: comparing interacting boson model configuration mixing and Gogny mean-field energy surfaces

The evolution of the total energy surface and the nuclear shape in the isotopic chain $^{172-194}$Pt are studied in the framework of the interacting boson model, including configuration mixing. The results are compared with a self-consistent Hartree-Fock-Bogoliubov calculation using the Gogny-D1S interaction and a good agreement between both approaches shows up. The evolution of the deformation parameters points towards the presence of two different coexisting configurations in the region 176 $\leq$ A $\leq$ 186.Comment: Submitted to PR

Dash Sylvereye:A WebGL-powered Library for Dashboard-driven Visualization of Large Street Networks

State-of-the-art open network visualization tools like Gephi, KeyLines, and Cytoscape are not suitable for studying street networks with thousands of roads since they do not support simultaneously polylines for edges, navigable maps, GPU-accelerated rendering, interactivity, and the means for visualizing multivariate data. To fill this gap, the present paper presents Dash Sylvereye: a new Python library to produce interactive visualizations of primal street networks on top of tiled web maps. Thanks to its integration with the Dash framework, Dash Sylvereye can be used to develop web dashboards around temporal and multivariate street data by coordinating the various elements of a Dash Sylvereye visualization with other plotting and UI components provided by the Dash framework. Additionally, Dash Sylvereye provides convenient functions to easily import OpenStreetMap street topologies obtained with the OSMnx library. Moreover, Dash Sylvereye uses WebGL for GPU-accelerated rendering when redrawing the road network. We conduct experiments to assess the performance of Dash Sylvereye on a commodity computer when exploiting software acceleration in terms of frames per second, CPU time, and frame duration. We show that Dash Sylvereye can offer fast panning speeds, close to 60 FPS, and CPU times below 20 ms, for street networks with thousands of edges, and above 24 FPS, and CPU times below 40 ms, for networks with dozens of thousands of edges. Additionally, we conduct a performance comparison against two state-of-the-art street visualization tools. We found Dash Sylvereye to be competitive when compared to the state-of-the-art visualization libraries Kepler.gl and city-roads. Finally, we describe a web dashboard application that exploits Dash Sylvereye for the analysis of a SUMO vehicle traffic simulation

Decoding Online Hate in the United States: A BERT-CNN Analysis of 36 Million Tweets from 2020 to 2022

Since its inception, social media has enabled people worldwide to connect with like-minded individuals and freely express their thoughts and opinions. However, its widespread nature has not only had an immeasurable impact on society but also presented significant challenges. One such challenge is online hate speech. Consequently, the identification of hate speech has recently gained considerable attention, ranging from reactive methods, such as classifying individual posts, to proactive strategies that utilize contextual information to decipher the complex lexicon of online discussions. Despite these efforts, current research lacks a comprehensive analysis of hate speech on Twitter during the crucial 2020-2022 period, marked by significant events such as the COVID-19 pandemic. In this paper, we present a BERT-based model for classifying hate speech. To this end, we collected 36 million tweets posted in the United States on Twitter during this period. We developed, trained, and tested a BERT-based Convolutional Neural Network (BERT-CNN), using it to classify the collected tweets. The classification of this dataset revealed a high incidence of targets motivated by ethnicity, with gender and nationality as other prominent categories. This work provides insightful data on the sentiments of individuals across the United States during the events of 2020-2022

Interfaz de consulta en idioma español para la búsqueda de información en un ambiente académico

98 páginas. Maestría en Ciencias de la Computación.En este trabajo se aborda un sistema de consulta en idioma español de México para la búsqueda de información de dominio académico, mediante un modelo de segmentación y construcción de recursos léxicos, así como un análisis y enriquecimiento de un sistema de ontologías modulares en un ambiente académico. El sistema de consulta es implementado para recibir como entrada preguntas en idioma español del tipo ¿Dónde?, ¿Cuándo? y ¿Quién?, las cuales permiten identificar una tupla ontológica para la consulta al sistema de ontologías y que no utilizan un módulo de traducción como los trabajos reportados en la literatura. La metodología implementada permite la identificación de patrones estructurales para la búsqueda en SQWRL en el sistema de ontologías. Se realizó una evaluación en el reconocimiento de voz y en las respuestas recibidas por parte del modelo semántico, las preguntas son extraídas de expertos en el dominio académico. En esta tesis se realiza una interfaz de consulta en idioma español dentro de un dominio académico con una precisión de 92 %. Su evaluación es una aportación dentro del procesamiento de lenguaje natural con lexicones especializados, patrones estructurales que permiten realizar la búsqueda dentro de un sistema de ontologías, con un enriquecimiento en sus propiedades, clases e individuos

Comunicaciones aplicadas a la teleoperación

Presentación de la plataforma experimental de teleoperación basada en el sistema Grips de Kraft Telerobotics y presentación del hardware desarrollado para su adaptación a una plataforma abierta. Exposición de las diferentes formas de conexión obtenidas a través del hardware desarrollado para cerrar un bucle de control en un sistema bilateral de teleoperación. Estudio de diferentes protocolos de comunicación muy extendidos como USB y Ethernet, explicando sus fundamentos principales y el funcionamiento básico, y su aplicación en la robótica, en particular en sistemas Bilaterales de Teleoperación con exigencias de tiempo real. Presentación de resultados obtenidos y comparación entre protocolos en diversas situaciones planteadas

Gestión documental y la automatización en los procesos del área de cuentas por pagar en las empresas de outsourcing, en la Av. Primavera, Monterrico - en el distrito de Surco - Lima - Perú 2019

El presente trabajo de investigación tuvo como finalidad determinar la relación que existe entre la gestión documental y la automatización en los procesos de cuentas por pagar en una empresa de outsourcing. La investigación desarrollo dos variables, la independiente: Gestión documental, y la dependiente: La automatización cada una de ellas con tres dimensiones. Para su elaboración, la metodología de investigación que se utilizó un método de estudio cuantitativo de nivel descriptiva, esto nos ayudó a recopilar información cuantificable para ser utilizada en el análisis estadístico. El tipo de muestreo fue censal, donde se consideró a los integrantes de una población de estudio, constituidas por 24 trabajadores de las empresas de outsourcing; se empleó como técnica la encuesta de elaboración propia para las dos variables y el instrumento fue el cuestionario conformado por 41 preguntas, que se han elaborado en relación con las dimensiones del modelo escala de Likert, con 05 alternativas de respuesta Nunca (1), Casi Nunca (2), A Veces (3), Casi Siempre (4), Siempre (5), y que el mismo ha sido validado por criterio de jueces. En base a los resultados obtenidos mediante el programa SPSS, se concluyó el nivel de correlación entre la gestión documental y la automatización es (r = 0.935); lo que significa que existe una correlación muy alta entre las variables